add train set

README.md

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 ---
 license: mit
+task_categories:
+- audio-classification
+language:
+- en
+tags:
+- biology
+- dog
+pretty_name: BpAE
+size_categories:
+- 10K<n<100K
 ---
+
+## 📦 Dataset Description
+
+This dataset is part of the **Barkopedia Challenge**: [https://uta-acl2.github.io/barkopedia.html](https://uta-acl2.github.io/barkopedia.html)
+
+Check training data on Hugging Face:
+👉 [ArlingtonCL2/Barkopedia_Dog_Sex_Classification_Dataset](https://huggingface.co/datasets/ArlingtonCL2/Barkopedia_Dog_Sex_Classification_Dataset/)
+
+This challenge provides a dataset of labeled dog bark audio clips:
+**29,347 total clips** of vocalizations from **156 individual dogs** across **5 breeds**:
+
+- **Shiba Inu**
+- **Husky**
+- **Chihuahua**
+- **German Shepherd**
+- **Pitbull**
+
+- **Training set**: **26,895** clips
+  - 13,567 female
+  - 13,328 male
+- **Test set**: **2,452** clips
+  - 1,272 female
+  - 1,180 male
+  - Among these,
+    - **980 clips (~40%)** are used for public leaderboard evaluation
+    - **1,472 clips (~60%)** are used for final private leaderboard evaluation
+    - The full 2,452 evaluation clips are included in the dataset, but only public clips yield visible scores.
+
+---
+
+### 🔖 Labels
+
+Each audio clip is annotated with the **sex of the dog** (`male` or `female`).
+
+The labels were **manually generated and verified** by the dataset curators.
+
+The `train_labels.csv` file provides the ground truth (correct labels) for the training set. It includes:
+
+- **audio_id**: The filename of the dog bark audio clip (e.g., `bark_2408`)
+- **pred_dog_sex**: The annotated sex of the dog (`male` or `female`)
+
+---
+
+## 🛠️ Setup Instructions
+
+You need to merge the training splits (train_0, train_1, train_2) into a single directory by running the provided script:
+
+```bash
+python merge_train_set.py
+```

merge_train_set.py

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+import os
+import shutil
+import time
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Use the correct folder names (train_0, train_1, train_2)
+source_folders = ["./train_splits/train_0", "./train_splits/train_1", "./train_splits/train_2"]
+dest_dir = os.path.join(script_dir, "train")
+
+os.makedirs(dest_dir, exist_ok=True)
+
+copied = skipped = errors = 0
+start = time.time()
+
+print(f"Script directory: {script_dir}")
+print(f"Destination folder: {dest_dir}")
+print("Starting copy process...\n")
+
+for relative in source_folders:
+    src_path = os.path.join(script_dir, relative)
+    base_name = os.path.basename(relative)
+
+    if not os.path.isdir(src_path):
+        print(f"Skipping missing folder: {src_path}")
+        continue
+
+    print(f"Processing folder: {src_path}")
+
+    for fname in os.listdir(src_path):
+        if not fname.lower().endswith(".wav"):
+            continue
+
+        src_file = os.path.join(src_path, fname)
+        dest_file = os.path.join(dest_dir, f"{fname}")
+
+        if os.path.exists(dest_file):
+            skipped += 1
+            continue
+
+        try:
+            shutil.copy2(src_file, dest_file)
+            copied += 1
+            if copied % 100 == 0:
+                print(f"Copied {copied} files so far...")
+        except Exception as e:
+            print(f"Failed to copy {src_file}: {e}")
+            errors += 1
+
+duration = time.time() - start
+print(f"\nCopied: {copied}, Skipped: {skipped}, Errors: {errors}")
+print(f"Time taken: {duration:.2f} seconds")

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