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from typing import List
import random
import argparse
import json
from huggingface_hub import hf_hub_download
from datasets import Dataset
from multi_token.constants import ROLE_ASSISTANT, ROLE_USER
PRETRAIN_PHRASES = [
"Repeat the content of the video <video>",
"What is occuring in the video? <video>",
"<video>. What happened?",
"Convert <video> to text",
"What is being depicted in <video>?",
"What is the content of <video>?",
"Describe what occurs in the video. <video>",
"What is the video about? <video>",
"<video>. Tell me what occurs in the video.",
"What is the video about? <video>",
"Give me a summary of <video>",
"<video>. Detail what is happening in the video.",
"Tell me about <video>",
]
def _timestamp_to_seconds(timestamp: str):
parts = timestamp.split(":")
seconds = float(parts[-1])
seconds += float(parts[-2]) * 60
seconds += float(parts[-3]) * 60 * 60
return seconds
def _write_convo(row) -> List:
video = {
"url": "https://www.youtube.com/watch?v=" + row["YoutubeID"],
"start_time": _timestamp_to_seconds(row["Start_timestamp"]),
"end_time": _timestamp_to_seconds(row["End_timestamp"]),
}
# test load, jk let it fail
# load_video(video)
example = {
"videos": [video],
}
phrase = random.choice(PRETRAIN_PHRASES)
example["messages"] = [
{
"role": ROLE_USER,
"content": phrase,
},
{
"role": ROLE_ASSISTANT,
"content": row["Caption"],
},
]
return example
def main(args):
path = hf_hub_download(
repo_id="OpenGVLab/InternVid", filename="caption.jsonl", repo_type="dataset"
)
rows = []
with open(path, "r") as f:
for line in f:
rows.append(json.loads(line))
print("Dataset size:", len(rows))
if len(rows) > args.max_examples:
rows = random.sample(rows, k=args.max_examples)
def gen(subset_rows):
for row in subset_rows:
try:
yield _write_convo(row)
except Exception as e:
print(e)
ds = Dataset.from_generator(gen, gen_kwargs={"subset_rows": rows}, num_proc=5)
ds.save_to_disk(args.output_folder)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-o", "--output_folder", type=str, default="/data/xclip-internvid-pretrain"
)
parser.add_argument("-n", "--max_examples", type=int, default=500_000)
args = parser.parse_args()
main(args)
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