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license: cc-by-nc-nd-4.0 |
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--- |
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[](https://arxiv.org/abs/2502.04128) |
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**Update (2025-02-07):** Our paper has been released! |
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This script is for merging tokenized speech datasets stored in memmap format. The input datasets can be combined to form larger training datasets. |
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```python |
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import numpy as np |
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import os |
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def merge_memmap_datasets(dataset_dirs, output_dir): |
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# Ensure the output directory exists |
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os.makedirs(output_dir, exist_ok=True) |
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# Dataset splits to be merged |
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splits = ['train', 'val'] |
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for split in splits: |
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shapes = [] |
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seq_len = None |
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total_samples = 0 |
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# Collect shapes of all datasets and check sequence length consistency |
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for dataset_dir in dataset_dirs: |
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shape_path = os.path.join(dataset_dir, f'{split}_input_ids_shape.npy') |
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if not os.path.exists(shape_path): |
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print(f"Warning: {split}_input_ids_shape.npy not found in {dataset_dir}, skipping this dataset.") |
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continue |
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shape = np.load(shape_path) |
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print(f"Loaded shape of {split} data from {dataset_dir}: {shape}") |
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shape = tuple(shape) |
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shapes.append((dataset_dir, shape)) |
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total_samples += shape[0] |
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if seq_len is None: |
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seq_len = shape[1] |
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elif seq_len != shape[1]: |
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print(f"Error: Sequence length mismatch in {split} data from {dataset_dir}.") |
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return |
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if total_samples == 0: |
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print(f"Error: No valid {split} data found for merging.") |
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continue |
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new_shape = (total_samples, seq_len) |
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# Create new memmap file |
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output_memmap_path = os.path.join(output_dir, f'{split}_input_ids.memmap') |
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output_memmap = np.memmap( |
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output_memmap_path, dtype='int32', mode='w+', shape=new_shape |
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) |
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# Copy data from each dataset to the new memmap file |
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start_idx = 0 |
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for dataset_dir, shape in shapes: |
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memmap_path = os.path.join(dataset_dir, f'{split}_input_ids.memmap') |
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data = np.memmap( |
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memmap_path, dtype='int32', mode='r', shape=shape |
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) |
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end_idx = start_idx + shape[0] |
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output_memmap[start_idx:end_idx, :] = data[:] |
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print(f"Merged {split} data from {dataset_dir} into positions {start_idx}:{end_idx}") |
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start_idx = end_idx |
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del data # Free memory |
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# Delete temporary variable and flush data to disk |
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del output_memmap |
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# Save the new shape file |
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np.save(os.path.join(output_dir, f'{split}_input_ids_shape.npy'), new_shape) |
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print(f"Completed merging {split} data. New shape: {new_shape}") |
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if __name__ == "__main__": |
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dataset_dirs = [ |
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'libriheavy_tts_1', |
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'libriheavy_tts_2', |
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'libriheavy_tts_3', |
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'libriheavy_tts_4', |
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'emilia_en_1', |
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'emilia_en_2', |
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'emilia_en_3', |
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'emilia_en_4', |
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] |
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output_dir = 'libriheavy_tts_all' |
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merge_memmap_datasets(dataset_dirs, output_dir) |
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``` |
