feat: add per-step timing to feature extraction logs

Each step now prints elapsed seconds on completion.
Total time printed at the end to identify bottlenecks.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/extract_features.py

@@ -10,6 +10,7 @@ Usage:
 import argparse
 import os
 import sys
+import time
 import numpy as np
 import torch
 
@@ -20,7 +21,21 @@ if _PLUGIN_DIR not in sys.path:
     sys.path.insert(0, _PLUGIN_DIR)
 
 
+def _step(n, total, label):
+    """Print step header and return start time."""
+    print(f"[extract] Step {n}/{total} — {label}...", flush=True)
+    return time.perf_counter()
+
+
+def _done(t0, extra=""):
+    elapsed = time.perf_counter() - t0
+    suffix = f"  {extra}" if extra else ""
+    print(f"[extract]   done in {elapsed:.1f}s{suffix}", flush=True)
+
+
 def main():
+    t_total = time.perf_counter()
+
     parser = argparse.ArgumentParser(description="PrismAudio feature extraction")
     parser.add_argument("--video", required=True, help="Path to input video")
     parser.add_argument("--cot_text", required=True, help="Chain-of-thought description")
@@ -46,23 +61,23 @@ def main():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     # ------------------------------------------------------------------
-    print("[extract] Step 1/6 — importing dependencies...", flush=True)
+    t0 = _step(1, 6, "importing dependencies")
     from data_utils.v2a_utils.feature_utils_288 import FeaturesUtils
     import torchvision.transforms as T
     from decord import VideoReader, cpu
-    print("[extract] Step 1/6 — done", flush=True)
+    _done(t0)
 
     # ------------------------------------------------------------------
-    print("[extract] Step 2/6 — loading models (T5, VideoPrism, Synchformer)...", flush=True)
+    t0 = _step(2, 6, "loading models (T5, VideoPrism, Synchformer)")
     feat_utils = FeaturesUtils(
         vae_config_path=args.vae_config,
         synchformer_ckpt=args.synchformer_ckpt,
         device=device,
     )
-    print("[extract] Step 2/6 — done", flush=True)
+    _done(t0)
 
     # ------------------------------------------------------------------
-    print("[extract] Step 3/6 — reading and preprocessing video...", flush=True)
+    t0 = _step(3, 6, "reading and preprocessing video")
     vr = VideoReader(args.video, ctx=cpu(0))
     fps = vr.get_avg_fps()
     total_frames = len(vr)
@@ -96,30 +111,26 @@ def main():
         T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
     ])
     sync_input = torch.stack([sync_transform(f) for f in sync_frames]).unsqueeze(0).to(device)
-    print("[extract] Step 3/6 — done", flush=True)
+    _done(t0)
 
     # ------------------------------------------------------------------
-    print("[extract] Step 4/6 — encoding text with T5-Gemma...", flush=True)
+    t0 = _step(4, 6, "encoding text with T5-Gemma")
     text_features = feat_utils.encode_t5_text([args.cot_text])
-    print(f"[extract]   text_features shape: {tuple(text_features.shape)}", flush=True)
-    print("[extract] Step 4/6 — done", flush=True)
+    _done(t0, f"shape={tuple(text_features.shape)}")
 
     # ------------------------------------------------------------------
-    print("[extract] Step 5/6 — encoding video with VideoPrism...", flush=True)
+    t0 = _step(5, 6, "encoding video with VideoPrism")
     global_video_features, video_features, global_text_features = \
         feat_utils.encode_video_and_text_with_videoprism(clip_input, [args.cot_text])
-    print(f"[extract]   global_video_features : {tuple(global_video_features.shape)}", flush=True)
-    print(f"[extract]   video_features        : {tuple(video_features.shape)}", flush=True)
-    print(f"[extract]   global_text_features  : {tuple(global_text_features.shape)}", flush=True)
-    print("[extract] Step 5/6 — done", flush=True)
+    _done(t0, f"video={tuple(video_features.shape)}  global={tuple(global_video_features.shape)}")
 
     # ------------------------------------------------------------------
-    print("[extract] Step 6/6 — encoding video with Synchformer...", flush=True)
+    t0 = _step(6, 6, "encoding video with Synchformer")
     sync_features = feat_utils.encode_video_with_sync(sync_input)
-    print(f"[extract]   sync_features shape: {tuple(sync_features.shape)}", flush=True)
-    print("[extract] Step 6/6 — done", flush=True)
+    _done(t0, f"shape={tuple(sync_features.shape)}")
 
     # ------------------------------------------------------------------
+    t0 = time.perf_counter()
     print(f"[extract] Saving features to {args.output} ...", flush=True)
     np.savez(
         args.output,
@@ -131,7 +142,8 @@ def main():
         caption_cot=args.cot_text,
         duration=duration,
     )
-    print(f"[extract] Done — features saved to {args.output}", flush=True)
+    print(f"[extract] Saved in {time.perf_counter() - t0:.1f}s", flush=True)
+    print(f"[extract] Total time: {time.perf_counter() - t_total:.1f}s", flush=True)
 
 
 if __name__ == "__main__":