Datasets:
Tasks:
Automatic Speech Recognition
Languages:
Swedish
Multilinguality:
monolingual
Size Categories:
100K<n<1M
License:
| import tarfile | |
| import os | |
| import shutil | |
| import multiprocessing as mp | |
| from pathlib import Path | |
| from tqdm import tqdm | |
| import pandas as pd | |
| df = pd.read_parquet("df_train_v2.parquet") | |
| df["filename_full"] = "/home/fatrek/data_network/faton/riksdagen_anforanden/data/rixvox_v2/" + df["filename"] | |
| df = df.rename(columns={"sex": "gender"}) | |
| # Group by intressent_id and count occurences of each id in the dataset, keep the columns speaker | |
| # and intressent_id and sort by the count of occurences | |
| df["speaker_total_hours"] = df.groupby(["speaker", "party"])["duration"].transform("sum") / 3600 | |
| df_hours = df.groupby(["speaker", "party"]).first().sort_values("speaker_total_hours", ascending=False).reset_index() | |
| df_hours = df_hours.sample(frac=1, random_state=1337) # Shuffle the rows | |
| # Set train equals to True until the cumulative sum of speaker_total_hours is 98% of the total | |
| df_hours["train"] = df_hours["speaker_total_hours"].cumsum() / df_hours["speaker_total_hours"].sum() < 0.98 | |
| # Valid equals True until cumulative sum is 1% of the total | |
| df_hours["valid"] = False | |
| df_hours.loc[df_hours["train"] == False, "valid"] = ( | |
| df_hours[df_hours["train"] == 0]["speaker_total_hours"].cumsum() / df_hours["speaker_total_hours"].sum() < 0.01 | |
| ) | |
| df_hours["test"] = (df_hours["train"] == False) & (df_hours["valid"] == False) # The rest is test | |
| # Create splits | |
| df_train = pd.merge(df, df_hours.loc[df_hours["train"], ["speaker", "party"]], on=["speaker", "party"], how="inner") | |
| df_valid = pd.merge(df, df_hours.loc[df_hours["valid"], ["speaker", "party"]], on=["speaker", "party"], how="inner") | |
| df_test = pd.merge(df, df_hours.loc[df_hours["test"], ["speaker", "party"]], on=["speaker", "party"], how="inner") | |
| def split_creator(df, observations_per_shard, shard_name): | |
| df["shard"] = range(0, len(df)) | |
| df["shard"] = df["shard"] // observations_per_shard | |
| df["shard"] = shard_name + "_" + df["shard"].astype(str) | |
| return df["shard"] | |
| df_train["shard"] = split_creator(df_train, 6500, "train") | |
| df_valid["shard"] = split_creator(df_valid, 6500, "dev") | |
| df_test["shard"] = split_creator(df_test, 6500, "test") | |
| df_train["nr_words"] = df_train["text"].str.split().str.len() | |
| df_train = df_train[df_train["nr_words"] <= 160].reset_index(drop=True) | |
| df_train = df_train.drop(columns="nr_words") | |
| def create_tar(df, data_folder="/home/fatrek/data_network/faton/rixvox/data"): | |
| shard_filename = df["shard"].reset_index(drop=True).values[0] | |
| shard_filename = shard_filename + ".tar.gz" | |
| split = df["shard"].reset_index(drop=True).str.extract(r"(.*)_")[0][0] # train_0 -> train | |
| os.makedirs(os.path.join(data_folder, split), exist_ok=True) | |
| print(f"Creating tarfile: {os.path.join(data_folder, split, shard_filename)}") | |
| with tarfile.open(os.path.join(data_folder, split, shard_filename), "w:gz") as tar: | |
| for filename in df["filename_full"].values: | |
| tar.add(Path(filename), arcname=Path(filename).relative_to(Path(filename).parent.parent), recursive=False) | |
| # Group by shard and split dataframes in to several dataframes in list | |
| groups = df_train.groupby("shard") | |
| df_train_list = [groups.get_group(x) for x in groups.groups] | |
| groups = df_valid.groupby("shard") | |
| df_valid_list = [groups.get_group(x) for x in groups.groups] | |
| groups = df_test.groupby("shard") | |
| df_test_list = [groups.get_group(x) for x in groups.groups] | |
| data_folder = "/home/fatrek/data_network/faton/RixVox/data" | |
| # for shard in df_train_list: | |
| # create_tar(shard, data_folder) | |
| with mp.Pool(16) as pool: | |
| pool.map(create_tar, df_train_list) | |
| with mp.Pool(1) as pool: | |
| pool.map(create_tar, df_valid_list) | |
| pool.map(create_tar, df_test_list) | |
| df_train = df_train.drop(columns=["shard", "filename_full", "file_size"]) | |
| df_valid = df_valid.drop(columns=["shard", "filename_full", "file_size"]) | |
| df_test = df_test.drop(columns=["shard", "filename_full", "file_size"]) | |
| df_train.to_parquet(os.path.join("data", "train_metadata.parquet"), index=False) | |
| df_valid.to_parquet(os.path.join("data", "dev_metadata.parquet"), index=False) | |
| df_test.to_parquet(os.path.join("data", "test_metadata.parquet"), index=False) | |