Upload filter_and_upload.py

filter_and_upload.py

@@ -0,0 +1,72 @@
+import json
+from datasets import load_dataset, Dataset
+
+def check_no_overlap_and_min_duration(utterances, min_duration=1.2):
+    """
+    Check that:
+    1. No overlap between consecutive utterances
+    2. Every utterance is at least min_duration seconds long
+    """
+    # Empty utterance list should not be considered valid.
+    if not utterances:
+        return False
+
+    prev_end = -float("inf")
+    for utt in utterances:
+        words = utt["words"]
+        if not words:
+            return False
+        utt_start = words[0]["start_time"]
+        utt_end = words[-1]["end_time"]
+
+        # Check no overlap with previous utterance
+        if utt_start < prev_end:
+            return False
+
+        # Check minimum duration
+        if utt_end - utt_start < min_duration:
+            return False
+
+        prev_end = utt_end
+    return True
+
+
+def main():
+    ds1 = load_dataset("humanify/si", name="naturalistic", split="test", streaming=True)
+    ds2 = load_dataset("humanify/si", name="improvised", split="test", streaming=True)
+    # merge
+    from itertools import chain
+    ds = chain(ds1, ds2)
+
+    selected = []
+    for sample in ds:
+        utterances = json.loads(sample["utterances_json"])
+        if check_no_overlap_and_min_duration(utterances):
+            selected.append(sample)
+            print(f"[{len(selected)}/100] Selected: {sample['conversation_id']}")
+            if len(selected) >= 50:
+                break
+
+    print(f"\nTotal selected: {len(selected)}")
+    if len(selected) < 50:
+        print("WARNING: Not enough samples meeting criteria!")
+
+    rows = {k: [] for k in selected[0].keys()}
+    for s in selected:
+        for k, v in s.items():
+            rows[k].append(v)
+
+    eval_ds = Dataset.from_dict(rows)
+    print(len(eval_ds))
+
+    if len(eval_ds) == 50:
+        print(f"\nDataset info: {eval_ds}")
+        print("Pushing to hub: humanify/si-eval-50 ...")
+        eval_ds.push_to_hub("humanify/si-eval-50", split="test")
+        print("Done!")
+    else:
+        print(len(eval_ds))
+
+
+if __name__ == "__main__":
+    main()