| import os | |
| import subprocess | |
| import pandas as pd | |
| direction_speech = os.getenv("DIRECTION_SPEECH", "enA") | |
| direction_text = os.getenv("DIRECTION_TEXT", "jpn") | |
| chunk_size = int(os.getenv("CHUNK_SIZE", 10)) | |
| url = f"https://dl.fbaipublicfiles.com/seamless/data/seamless.dataset.metadata.public.{direction_speech}-{direction_text}.withduration.tsv.gz" | |
| filename = os.path.basename(url) | |
| subprocess.run(["wget", url, "-O", filename]) | |
| df = pd.read_csv(filename, sep='\t', header=None, dtype=str) | |
| df.columns = ["cc_warc", "cc_sha", "cc_document_url", "cc_lineno", "paragraph_digest", "sentence_digest", "text_lid_score", "laser_score", "direction", "side", "line_no"] | |
| df = df[df.side == direction_text] | |
| df["cc_lineno"] = df["cc_lineno"].astype(int) | |
| df.sort_values(by=["cc_warc", "cc_sha", "cc_document_url", "cc_lineno"], inplace=True) | |
| batch_size = int(len(df)/chunk_size) | |
| start = 0 | |
| end = batch_size | |
| index = 1 | |
| while start != end: | |
| df.iloc[start:end].to_csv(f"seamless.dataset.metadata.public.{direction_speech}-{direction_text}.withduration.reordered.batch_{index}.tsv", sep="\t", index=False, header=False) | |
| index += 1 | |
| start = end | |
| end += batch_size | |
| end = min(len(df), end) | |