TurnMaster_processing/curate_dataset.py

def curate_dataset(
    min_dur,
    max_dur,
    max_wer,
    csv1_path,
    csv2_path,
    full_dataset_path,
    output_path=None,
):
    if output_path is None:
        wer_str = str(max_wer).replace(".", "")
        output_path = (
            f"manifest_filtered_{min_dur}_{max_dur}_{wer_str}.csv"
        )

    # --- Read CSVs ---
    df1 = pd.read_csv(csv1_path)
    df2 = pd.read_csv(csv2_path)

    # --- Ensure numeric types ---
    for df in (df1, df2):
        df["duration"] = pd.to_numeric(df["duration"], errors="coerce")
        df["wer"] = pd.to_numeric(df["wer"], errors="coerce")

    # --- Apply filters on each CSV ---
    f1 = df1[
        (df1["duration"] > min_dur)
        & (df1["duration"] < max_dur)
        & (df1["wer"] < max_wer)
    ]

    f2 = df2[
        (df2["duration"] > min_dur)
        & (df2["duration"] < max_dur)
        & (df2["wer"] < max_wer)
    ]

    # --- Keep audio_id present in both filtered CSVs ---
    ids1 = set(f1["audio_id"].dropna().astype(str))
    ids2 = set(f2["audio_id"].dropna().astype(str))
    kept_ids = ids1.intersection(ids2)

    print(f"Kept audio_id count: {len(kept_ids)}")

    # --- Filter the full dataset ---
    full_df = pd.read_csv(full_dataset_path)
    full_df["audio_id"] = full_df["audio_id"].astype(str)

    filtered_full_df = full_df[full_df["audio_id"].isin(kept_ids)]

    # --- Save result ---
    filtered_full_df.to_csv(output_path, index=False)

    print(f"Filtered dataset saved to: {output_path}")
    print(f"Rows kept in full dataset: {len(filtered_full_df)}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Filter the dataset using duration and WER thresholds."
    )

    parser.add_argument("--min-dur", type=float, required=True)
    parser.add_argument("--max-dur", type=float, required=True)
    parser.add_argument("--max-wer", type=float, required=True)

    parser.add_argument("--csv1-path", required=True)
    parser.add_argument("--csv2-path", required=True)
    parser.add_argument("--full-dataset-path", required=True)
    parser.add_argument("--output-path", default=None)

    args = parser.parse_args()

    curate_dataset(
        min_dur=args.min_dur,
        max_dur=args.max_dur,
        max_wer=args.max_wer,
        csv1_path=args.csv1_path,
        csv2_path=args.csv2_path,
        full_dataset_path=args.full_dataset_path,
        output_path=args.output_path,
    )