Add causal person fusion and MuVAP stage-3 baselines

third_party/MuVAP/preprocess/vap/utils.py

@@ -0,0 +1,98 @@
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import List
+
+SAMPLE_RATE: int = 16000
+VAL_GROUPS = {"000", "016", "032", "048", "096", "112"}
+TEST_GROUPS = {"064", "080"}
+
+
+@dataclass(slots=True)
+class ProcessConfig:
+    fisher_path: str
+    val_groups: set[str]
+    test_groups: set[str]
+    active_splits: set[str]
+    input_file: str = ""
+
+
+def get_dataset_split(group: str, cfg: ProcessConfig):
+    if group in cfg.val_groups:
+        return "val"
+    if group in cfg.test_groups:
+        return "test"
+    return "train"
+
+
+def print_dataset_summary(events: List, is_turn_event=True):
+    stats = defaultdict(lambda: defaultdict(int))
+    categories = set()
+
+    for e in events:
+        split = get_dataset_split(
+            e.group, ProcessConfig("", VAL_GROUPS, TEST_GROUPS, set())
+        )
+
+        cat = f"{e.timing} {e.label}" if is_turn_event else e.label
+        categories.add(cat)
+        stats[split][cat] += 1
+        stats["TOTAL"][cat] += 1
+
+    sorted_cats = sorted(list(categories))
+
+    header = f"{'SPLIT':<10}"
+    for cat in sorted_cats:
+        header += f" | {cat:<15}"
+    header += f" | {'TOTAL'}"
+
+    width = len(header)
+    print(f"\n{'=' * width}")
+    print(f"{'DATASET EVENT SUMMARY':^{width}}")
+    print(f"{'=' * width}")
+    print(header)
+    print(f"{'-' * width}")
+
+    for split in ["train", "val", "test", "TOTAL"]:
+        row = f"{split.upper():<10}"
+        total = 0
+        for cat in sorted_cats:
+            val = stats[split][cat]
+            total += val
+            row += f" | {val:<15}"
+        row += f" | {total}"
+        print(row)
+    print(f"{'=' * width}\n")
+
+
+EVENT_SEG_LEN_SEC: int = 10
+EVENT_PADDING_SEC: float = 0.1
+
+
+def event_crop(
+    timing: str,
+    region_start: float,
+    region_end: float,
+    seg_len_sec: float = EVENT_SEG_LEN_SEC,
+    padding_sec: float = EVENT_PADDING_SEC,
+):
+    """Audio window an evaluation clip covers, or None if the event has none.
+
+    The window ends just before the decision point: a hair into the silence for
+    a SILENT event, a hair before the speech ends for an ACTIVE one. This is the
+    single definition of that window - `02_prep_event.py` writes clips with it
+    and the raw dataloader reads the same span straight from the source audio,
+    so the two cannot drift apart.
+    """
+    if timing == "SILENT":
+        crop_end = region_start + padding_sec
+        if crop_end > region_end:
+            return None
+    elif timing == "ACTIVE":
+        crop_end = region_end - padding_sec
+    else:
+        return None
+
+    crop_start = crop_end - seg_len_sec
+    if crop_start < 0:
+        return None
+    return crop_start, crop_end