Add compress tool

preparing_data/wenet_clean/compress_tar.py

+import tarfile
+from pathlib import Path
+from tqdm import tqdm
+import io
+import random
+from multiprocessing import Process
+
+
+def chunked_tarring(rank, file_list, base_folder, output_folder, chunk_size=1024**3):
+    chunk_count = 1
+    total_size = 0
+    saved_count = 0
+
+    buffer = io.BytesIO()
+    tar = tarfile.open(fileobj=buffer, mode="w")
+
+    for audio_file in file_list:
+        txt_file = audio_file.with_suffix(".txt")
+        if not txt_file.exists():
+            continue
+
+        file_size = audio_file.stat().st_size + txt_file.stat().st_size
+        if total_size + file_size > chunk_size:
+            tar.close()
+
+            # write the buffer to disk
+            buffer.seek(0)
+            with open(output_folder / f"chunk-{rank:03d}-{chunk_count:04d}.tar", "wb") as f:
+                f.write(buffer.read())
+
+            chunk_count += 1
+            total_size = 0
+            buffer = io.BytesIO()
+            tar = tarfile.open(fileobj=buffer, mode="w")
+
+        tar.add(audio_file, arcname=audio_file.relative_to(base_folder))
+        tar.add(txt_file, arcname=txt_file.relative_to(base_folder))
+
+        total_size += file_size
+
+        if saved_count % 1000 == 0:
+            print(f"Rank {rank}: {saved_count}/{len(file_list)}")
+        
+        saved_count += 1
+
+    tar.close()
+    buffer.seek(0)
+    with open(output_folder / f"chunk-{rank:03d}-{chunk_count:04d}.tar", "wb") as f:
+        f.write(buffer.read())
+    
+    print(f"Rank {rank}: {saved_count}/{len(file_list)}")
+
+
+if __name__ == "__main__":
+    base_folder = Path("/mnt/nvme1/multi-modal-test/WenetSpeech/cleaned")
+    output_folder = Path("/mnt/nvme1/multi-modal-test/WenetSpeech/compressed")
+    output_folder.mkdir(exist_ok=True, parents=True)
+    num_workers = 50
+
+    file_list = list(tqdm(base_folder.rglob("*.flac")))
+    random.shuffle(file_list)
+    print(f"Total files: {len(file_list)}")
+
+    chunk_size = len(file_list) // num_workers
+    processes = []
+
+    for i in range(num_workers):
+        start = i * chunk_size
+        end = (i + 1) * chunk_size
+        if i == num_workers - 1:
+            end = len(file_list)
+
+        p = Process(target=chunked_tarring, args=(i, file_list[start:end], base_folder, output_folder))
+        p.start()
+        processes.append(p)
+
+    for p in processes:
+        p.join()
+
+    print("Done")