<jupyter_start><jupyter_text>validate splits<jupyter_code>for label in set(metadata.label):
    print(f'train label {label} length (sec):', calc_sum_column(metadata[metadata['split_type'] == 'train'], label))
    print(f'val label {label} length (sec):', calc_sum_column(metadata[metadata['split_type'] == 'val'], label))
metadata.to_csv(splited_metadata_path, index=False)
import pandas as pd
training_meta = pd.read_csv('orcasound/orcasound_bg_from_calls.csv')
ref_meta = pd.read_csv('orcasound/orcasound_train_val_splits.csv')
len(training_meta)
len(ref_meta)
training_meta[training_meta['label'] == 1]['call_length'].sum()
ref_meta[ref_meta['label'] == 1]['call_length'].sum()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1619718309859155, 'fraction_numerical': 0.0028169014084507044, 'mean_word_length': 10.655737704918034, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '521994', 'license': 'Apache-2.0', 'n_tokens_mistral': 255, 'n_tokens_neox': 251, 'n_words': 37, 'path': 'datasets/split_metadata.ipynb', 'repo_name': 'deep-voice/soundbay', 'size': '710'}