import json import os import tarfile import zipfile import gzip import subprocess import time from os.path import join as p_join from tqdm import tqdm from multiprocessing import Pool from typing import Optional, Dict from glob import glob import pandas as pd import soundfile as sf from datasets import Dataset, Audio, DatasetDict audio_loader = Audio() # dataset config url_metadata_dict = { "enA-jaA": "https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.enA-jaA.tsv.gz", "enA-jpn": "https://dl.fbaipublicfiles.com/seamless/data/seamless.dataset.metadata.public.enA-jpn.withduration.tsv.gz" } direction = os.getenv("DIRECTION", "enA-jaA") sides = set(direction.split("-")) cache_dir_audio = p_join("download", "audio", direction) cache_dir_feature = p_join("download", "feature", direction) os.makedirs(cache_dir_feature, exist_ok=True) for s in sides: os.makedirs(p_join(cache_dir_audio, s), exist_ok=True) # processor config n_pool = int(os.getenv("N_POOL", os.cpu_count())) wget_max_retry = os.getenv("MAX_RETRY", "2") wget_timeout = os.getenv("TIMEOUT", "30") line_no_start = int(os.getenv("LINE_NO_START", 0)) line_no_end = int(os.getenv("LINE_NO_END", 10000)) dataset_id = os.getenv("DATASET_ID", 0) hf_org = os.getenv("HF_ORG", "asahi417") hf_dataset = f"seamless-align-{direction}" def wget(url: str, output_file: Optional[str] = None): os.makedirs(os.path.dirname(output_file), exist_ok=True) subprocess.run(["wget", url, "-O", output_file, "--tries", wget_max_retry, "--timeout", wget_timeout]) if not os.path.exists(output_file): return False if output_file.endswith('.tar.gz') or output_file.endswith('.tgz') or output_file.endswith('.tar'): if output_file.endswith('.tar'): tar = tarfile.open(output_file) else: tar = tarfile.open(output_file, "r:gz") tar.extractall(os.path.dirname(output_file)) tar.close() os.remove(output_file) elif output_file.endswith('.gz'): with gzip.open(output_file, 'rb') as f: with open(output_file.replace('.gz', ''), 'wb') as f_write: f_write.write(f.read()) os.remove(output_file) elif output_file.endswith('.zip'): with zipfile.ZipFile(output_file, 'r') as zip_ref: zip_ref.extractall() os.remove(output_file) return True def get_metadata(): url_metadata = url_metadata_dict[direction] meta_data_filename = os.path.basename(url_metadata) meta_data_path = p_join("download", "meta", meta_data_filename) if not os.path.exists(meta_data_path.replace(".gz", "")): assert wget(url_metadata, output_file=meta_data_path) df = pd.read_csv(meta_data_path.replace(".gz", ""), sep=r'[\t\s]', header=None) df = df[[0, 2, 3, 4, 9, 10, 11, 12]] df.columns = ["id", "url", "duration_start", "duration_end", "laser_score", "direction", "side", "line_no"] if direction == "enA-jpn": df = df[df["side"] == "enA"] assert len(df["direction"].unique()) == 1 df.pop("direction") return df.sort_values(by=["line_no", "side"]) def to_json_serializable(val): if "float" in str(type(val)): return float(val) if "int" in str(type(val)): return int(val) return str(val) def get_audio(dataframe: pd.DataFrame): features = {"line_no": int(dataframe.pop('line_no').values[0])} for side, df in dataframe.groupby("side"): df.pop("side") features.update({f"{side}.{k}": to_json_serializable(v) for k, v in df.iloc[0].to_dict().items()}) identifier = os.path.basename(features[f"{side}.url"]).split(".")[-1] features[f"{side}.path"] = str(p_join(cache_dir_audio, side, f"{features['line_no']}.{identifier}")) start, end = features[f"{side}.duration_start"], features[f"{side}.duration_end"] if not os.path.exists(features[f"{side}.path"]): flag = wget(features[f"{side}.url"], output_file=features[f"{side}.path"]) if not flag: return False else: try: wav = audio_loader.decode_example({"path": features[f"{side}.path"], "bytes": None}) if start < end < len(wav["array"]): sf.write(features[f"{side}.path"], wav["array"][start:end], wav["sampling_rate"]) else: os.remove(features[f"{side}.path"]) return False except Exception as e: print(e) os.remove(features[f"{side}.path"]) return False with open(p_join(cache_dir_feature, f'{features["line_no"]}.json'), "w") as f: json.dump(features, f) return True def process_dataset(): df_metadata = get_metadata() print(f"metadata: {len(df_metadata)}, {line_no_start} --> {line_no_end}") inputs = [ g for line_no, g in df_metadata.groupby("line_no") if line_no_start <= line_no < line_no_end and not os.path.exists( p_join(cache_dir_feature, f'{int(line_no)}.json') ) ] print(f"filtered unique lines: {len(inputs)}") if direction == "enA-jaA": inputs = [g for g in inputs if len(g["side"].unique()) == 2 and set(g["side"].unique()) == sides] print(f"removed side != 2: {len(inputs)}") if n_pool == 1: for g in tqdm(inputs, total=len(inputs)): flag = get_audio(g) if not flag: print(f"failed:\n{g['url']}") else: with Pool(n_pool) as pool: pool.map(get_audio, tqdm(inputs, total=len(inputs))) def loader(feature: str) -> Dict: with open(feature) as f_reader: return json.load(f_reader) features = [loader(i) for i in glob(p_join(cache_dir_feature, '*.json'))] features = [i for i in features if line_no_start <= int(i["line_no"]) < line_no_end] print(f"push {len(features)} records to hub") data_dict = {} for side in sides: data_dict.update({f"{side}.audio": [i.pop(f"{side}.path") for i in features]}) data_dict.update({k: [i[k] for i in features] for k in features[0].keys()}) audio_dataset = Dataset.from_dict(data_dict) for side in sides: audio_dataset = audio_dataset.cast_column(f"{side}.audio", Audio()) dataset_to_push = DatasetDict({"train": audio_dataset}) repo_name = f"{hf_org}/{hf_dataset}" while True: try: dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}") break except Exception: print(f"FAILED: push_to_hub on {repo_name} failed. wait 60 sec and retry soon...") time.sleep(60) if __name__ == '__main__': process_dataset()