|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import datasets |
|
from pathlib import Path |
|
import pandas as pd |
|
import os |
|
from typing import Optional, List |
|
from multiprocessing import Lock, Process |
|
from tqdm import tqdm |
|
|
|
try: |
|
import yt_dlp |
|
except ImportError: |
|
raise ImportError("this code must need yt-dlp package, please run `pip install yt-dlp`") |
|
|
|
|
|
_CITATION = """\ |
|
@inproceedings{audiocaps, |
|
title={AudioCaps: Generating Captions for Audios in The Wild}, |
|
author={Kim, Chris Dongjoo and Kim, Byeongchang and Lee, Hyunmin and Kim, Gunhee}, |
|
booktitle={NAACL-HLT}, |
|
year={2019} |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
We explore audio captioning: generating natural language description for any kind of audio in the wild. We contribute AudioCaps, a large-scale dataset of about 46K audio clips to human-written text pairs collected via crowdsourcing on the AudioSet dataset. The collected captions of AudioCaps are indeed faithful for audio inputs. We provide the source code of the models to explore what forms of audio representation and captioning models are effective for the audio captioning. |
|
""" |
|
|
|
_HOMEPAGE = "https://github.com/cdjkim/audiocaps" |
|
|
|
_LICENSE = "MIT License" |
|
|
|
|
|
_URL = "https://raw.githubusercontent.com/cdjkim/audiocaps/master/dataset/" |
|
_URLs = { |
|
"train": _URL + "train.csv", |
|
"valid": _URL + "val.csv", |
|
"test": _URL + "test.csv", |
|
} |
|
|
|
YT_URL = "https://www.youtube.com/watch?v=" |
|
DURATION = os.getenv("AUDIOCAPS_DURATION", 10) |
|
|
|
|
|
|
|
class AudioCaps(datasets.GeneratorBasedBuilder): |
|
"""Korean Naver movie review dataset.""" |
|
|
|
VERSION = datasets.Version("1.0.0") |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"audiocap_id": datasets.Value("int32"), |
|
"youtube_id": datasets.Value("string"), |
|
"start_time": datasets.Value("int32"), |
|
"audio": datasets.Audio(48000), |
|
"caption": datasets.Value("string"), |
|
} |
|
), |
|
supervised_keys=None, |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
downloaded_files = dl_manager.download_and_extract(_URLs) |
|
num_workers = os.getenv("AUDIOCAPS_NUM_WORKER", 4) |
|
|
|
for split, filepath in downloaded_files.items(): |
|
download_dir = Path(filepath).parent |
|
save_dir = download_dir.joinpath("AudioCaps", split) |
|
|
|
df = pd.read_csv(filepath) |
|
|
|
df_chunks = self.df_to_n_chunks(df, num_workers) |
|
ps_ls = list() |
|
for i in range(num_workers): |
|
process = Process( |
|
target=self.yt_dlp_processor, |
|
args=(df_chunks[i], save_dir, i), |
|
) |
|
process.start() |
|
ps_ls.append(process) |
|
|
|
try: |
|
for ps in ps_ls: |
|
ps.join() |
|
except KeyboardInterrupt: |
|
for ps in ps_ls: |
|
ps.terminate() |
|
ps.join() |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": downloaded_files["train"], |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": downloaded_files["valid"], |
|
"split": "valid", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": downloaded_files["test"], |
|
"split": "test", |
|
}, |
|
), |
|
] |
|
|
|
|
|
def yt_dlp_processor( |
|
self, |
|
df: pd.DataFrame, |
|
audios_dir: Path, |
|
pid: Optional[int] = 0, |
|
): |
|
""" |
|
download yt videos specified in df, can be used in a multiprocess manner, |
|
just specify lock parameter, so logging goes safe |
|
""" |
|
print(f"### Download process number {pid} started") |
|
for row in tqdm(list(df.iterrows()), desc=f"dw_ps: {pid}"): |
|
wav_file = audios_dir.joinpath(f"""{row[1]["youtube_id"]}.wav""") |
|
if wav_file.exists(): |
|
continue |
|
|
|
youtube_id = row[1]["youtube_id"] |
|
start_time = row[1]["start_time"] |
|
end_time = start_time + DURATION |
|
|
|
dw_url = f"{YT_URL}{youtube_id}" |
|
|
|
|
|
audio_name = os.path.join(audios_dir, f"{youtube_id}") |
|
os.system( |
|
f"""yt-dlp -S "asr:48000" -x --quiet --audio-format wav --external-downloader aria2c --external-downloader-args 'ffmpeg_i:-ss {start_time} -to {end_time}' -o '{audio_name}.%(ext)s' {dw_url}""" |
|
) |
|
|
|
def df_to_n_chunks(self, df: pd.DataFrame, n: int) -> List[pd.DataFrame]: |
|
chunk_size = len(df) // n + 1 |
|
dfs = [] |
|
for i in range(n): |
|
new_df = df.iloc[i * chunk_size : (i + 1) * chunk_size] |
|
dfs.append(new_df) |
|
return dfs |
|
|
|
def _generate_examples(self, filepath, split): |
|
rm_flag = os.getenv("AUDIOCAPS_RM_AUDIO_FILE", False) |
|
save_flag = os.getenv("AUDIOCAPS_SAVE_TO_BYTE", False) |
|
|
|
df = pd.read_csv(filepath) |
|
download_dir = Path(filepath).parent |
|
save_dir = download_dir.joinpath("AudioCaps", split) |
|
|
|
for id_, row in df.iterrows(): |
|
wav_file = save_dir.joinpath(f"""{row["youtube_id"]}.wav""") |
|
if not wav_file.exists(): |
|
continue |
|
audio = wav_file.read_bytes() if save_flag else str(wav_file) |
|
if rm_flag: |
|
|
|
os.remove(wav_file) |
|
yield id_, { |
|
"audiocap_id": row["audiocap_id"], |
|
"youtube_id": row["youtube_id"], |
|
"start_time": row["start_time"], |
|
"audio": audio, |
|
"caption": row["caption"], |
|
} |
|
if rm_flag: |
|
|
|
os.remove(save_dir) |
|
|