OPTIMIZE: Improve sample length computation by using batch column access

src/voxcpm/training/data.py

@@ -70,25 +70,28 @@ def compute_sample_lengths(
         duration(s) * audio_vae_fps -> 近似 VAE 帧数 t_vae
         t_seq = ceil(t_vae / patch_size)
     - 序列总长约为: text_len + t_seq + 2
+    
+    Optimized: Use batch column access instead of iterating item by item.
     """
-    lengths: List[int] = []
+    # Batch access columns - much faster than per-item access
+    text_ids_list = ds["text_ids"]
+    text_lens = [len(t) for t in text_ids_list]
     
     has_duration = "duration" in ds.column_names
-
-    for i in range(len(ds)):
-        item = ds[i]
-        text_len = len(item["text_ids"])
-
-        # 音频时长（尽量不解码；若 manifest 里已有 duration 列则优先使用）
     if has_duration:
-            duration = float(item["duration"])
+        durations = ds["duration"]
     else:
-            audio = item[DEFAULT_AUDIO_COLUMN]
-            duration = len(audio["array"]) / float(audio["sampling_rate"])
+        # Fallback: need to compute from audio (slow, but unavoidable without duration column)
+        durations = []
+        for i in range(len(ds)):
+            audio = ds[i][DEFAULT_AUDIO_COLUMN]
+            durations.append(len(audio["array"]) / float(audio["sampling_rate"]))
     
-        t_vae = math.ceil(duration * audio_vae_fps)
+    # Vectorized length computation
+    lengths = []
+    for text_len, duration in zip(text_lens, durations):
+        t_vae = math.ceil(float(duration) * audio_vae_fps)
         t_seq = math.ceil(t_vae / patch_size)
-
         total_len = text_len + t_seq + 2
         lengths.append(total_len)
 
@@ -211,4 +214,3 @@ def build_dataloader(
         collate_fn=HFVoxCPMDataset.collate_fn,
         drop_last=drop_last,
     )
-