Update README.md

README.md

@@ -1,4 +1,84 @@
 ---
 license: cc-by-4.0
 ---
-Coming Soon
+Coming Soon
+
+ 
+
+This script is for merging  tokenized speech datasets stored in memmap format. The input datasets can be combined to form larger training datasets. 
+```python
+import numpy as np
+import os
+
+def merge_memmap_datasets(dataset_dirs, output_dir):
+    # Ensure the output directory exists
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Dataset splits to be merged
+    splits = ['train', 'val']
+
+    for split in splits:
+        shapes = []
+        seq_len = None
+        total_samples = 0
+
+        # Collect shapes of all datasets and check sequence length consistency
+        for dataset_dir in dataset_dirs:
+            shape_path = os.path.join(dataset_dir, f'{split}_input_ids_shape.npy')
+            if not os.path.exists(shape_path):
+                print(f"Warning: {split}_input_ids_shape.npy not found in {dataset_dir}, skipping this dataset.")
+                continue
+            shape = np.load(shape_path)
+            print(f"Loaded shape of {split} data from {dataset_dir}: {shape}")
+            shape = tuple(shape)
+            shapes.append((dataset_dir, shape))
+            total_samples += shape[0]
+            if seq_len is None:
+                seq_len = shape[1]
+            elif seq_len != shape[1]:
+                print(f"Error: Sequence length mismatch in {split} data from {dataset_dir}.")
+                return
+
+        if total_samples == 0:
+            print(f"Error: No valid {split} data found for merging.")
+            continue
+
+        new_shape = (total_samples, seq_len)
+
+        # Create new memmap file
+        output_memmap_path = os.path.join(output_dir, f'{split}_input_ids.memmap')
+        output_memmap = np.memmap(
+            output_memmap_path, dtype='int32', mode='w+', shape=new_shape
+        )
+
+        # Copy data from each dataset to the new memmap file
+        start_idx = 0
+        for dataset_dir, shape in shapes:
+            memmap_path = os.path.join(dataset_dir, f'{split}_input_ids.memmap')
+            data = np.memmap(
+                memmap_path, dtype='int32', mode='r', shape=shape
+            )
+            end_idx = start_idx + shape[0]
+            output_memmap[start_idx:end_idx, :] = data[:]
+            print(f"Merged {split} data from {dataset_dir} into positions {start_idx}:{end_idx}")
+            start_idx = end_idx
+            del data  # Free memory
+
+        # Delete temporary variable and flush data to disk
+        del output_memmap
+
+        # Save the new shape file
+        np.save(os.path.join(output_dir, f'{split}_input_ids_shape.npy'), new_shape)
+
+        print(f"Completed merging {split} data. New shape: {new_shape}")
+
+if __name__ == "__main__":
+    dataset_dirs = [
+        'libriheavy_tts_1',
+        'libriheavy_tts_2',
+        'libriheavy_tts_3',
+        'libriheavy_tts_4'
+    ]
+    output_dir = 'libriheavy_tts_all'
+    merge_memmap_datasets(dataset_dirs, output_dir)
+```