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ManishThota
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5fb8331
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0c1e7d0
Upload 6 files
Browse files- Train.csv +382 -0
- app.py +48 -0
- requirements.txt +9 -0
- src/__init__.py +0 -0
- src/utils.py +50 -0
- src/video_model.py +66 -0
Train.csv
ADDED
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1 |
+
"","name","screen.interaction_yes","hands.free","indoors","standing","semType","FreqNewsScape","fesqCOCA","morphosyntax","syllNum","syllStress","tv.genre"
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2 |
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"1","2016-01-02_0700_US_KOCE_Tavis_Smiley_385.83-390.14_ago",0,1,1,0,"past",371.01,294.79,"adverb",2,"0 1","conversation"
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3 |
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4 |
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"3","2016-01-02_0837_US_KNBC_Late_Night_with_Seth_Myers_374.72-378.92_ago",0,1,1,0,"past",371.01,294.79,"adverb",2,"0 1","speech"
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5 |
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"4","2016-01-01_0100_UK_KCET_BBC_World_News_America_218.99-223.26_ago",0,0,1,0,"past",371.01,294.79,"adverb",2,"0 1","news"
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6 |
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"5","2016-01-04_0200_US_KABC_Eyewitness_News_6PM_1483.87-1488.07_ago",0,1,1,0,"past",371.01,294.79,"adverb",2,"0 1","news"
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7 |
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"6","2016-01-01_2300_US_WEWS_NewsChannel_5_at_6pm_29.89-34.2_ago",0,0,0,1,"past",371.01,294.79,"adverb",2,"0 1","news"
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8 |
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"7","2016-01-02_0700_US_KOCE_Tavis_Smiley_1291.38-1295.68_ago",0,1,1,0,"past",371.01,294.79,"adverb",2,"0 1","conversation"
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9 |
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"8","2016-06-29_0100_UK_KCET_BBC_World_News_1413.88-1418.3_at_present",0,1,1,1,"present",0.252,4.68,"advPhrase",3,"0 1 0","speech"
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10 |
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11 |
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12 |
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13 |
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14 |
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18 |
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25 |
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27 |
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33 |
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36 |
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"35","2016-01-20_0700_US_KNBC_Channel_4_News_at_11PM_1450.62-1455.06_currently",0,1,1,1,"present",37.025,65.94,"adverb",3,"1 0 0","news"
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37 |
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"36","2016-01-14_0200_US_KABC_Eyewitness_News_6PM_465.32-469.88_currently",0,0,0,1,"present",37.025,65.94,"adverb",3,"1 0 0","news"
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38 |
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"37","2016-01-07_1600_US_KCBS_This_Morning_1688.67-1693.15_currently",0,1,1,1,"present",37.025,65.94,"adverb",3,"1 0 0","other"
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39 |
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40 |
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41 |
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42 |
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43 |
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45 |
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|
46 |
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47 |
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|
48 |
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53 |
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376 |
+
"375","2016-01-04_1400_US_HLN_HLN_Morning_Express_1459.14-1463.72_yesterday",0,1,1,0,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","news"
|
377 |
+
"376","2016-01-05_0735_US_KNBC_Tonight_Show_with_Jimmy_Fallon_230.15-234.47_yesterday",0,1,1,1,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","speech"
|
378 |
+
"377","2016-01-05_0735_US_KABC_Jimmy_Kimmel_Live_305.05-309.66_yesterday",0,1,1,1,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","speech"
|
379 |
+
"378","2016-01-03_1500_US_KABC_Today_in_LA_Weekend_1.3-5.92_yesterday",1,0,1,1,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","weather"
|
380 |
+
"379","2016-01-05_0735_US_KABC_Jimmy_Kimmel_Live_308.47-312.92_yesterday",0,1,1,1,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","speech"
|
381 |
+
"380","2016-01-01_2359_US_WKYC_Channel_3_News_at_7_1256.73-1261.2_yesterday",0,0,1,1,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","weather"
|
382 |
+
"381","2016-01-02_0200_UK_KCET_BBC_World_News_696.86-701.54_yesterday",0,1,1,1,"past",266.16,65.5543959325253,"adverb",3,"1 0 0","news"
|
app.py
ADDED
@@ -0,0 +1,48 @@
|
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|
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|
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|
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|
|
|
1 |
+
# Importing the requirements
|
2 |
+
import warnings
|
3 |
+
warnings.filterwarnings("ignore")
|
4 |
+
|
5 |
+
import gradio as gr
|
6 |
+
from src.video_model import describe_video
|
7 |
+
|
8 |
+
|
9 |
+
# Video and text inputs for the interface
|
10 |
+
video = gr.Video(label="Video")
|
11 |
+
query = gr.Textbox(label="Question", placeholder="Enter your question here")
|
12 |
+
|
13 |
+
# Output for the interface
|
14 |
+
response = gr.Textbox(label="Predicted answer", show_label=True, show_copy_button=True)
|
15 |
+
|
16 |
+
# Examples for the interface
|
17 |
+
examples = [
|
18 |
+
[
|
19 |
+
"./videos/2016-01-01_0000_US_KNBC_The_Ellen_DeGeneres_Show_91.07-95.45_today.mp4",
|
20 |
+
"Here are some frames of a video. Describe this video in detail.",
|
21 |
+
],
|
22 |
+
[
|
23 |
+
"./videos/videos/2016-01-01_0100_UK_KCET_BBC_World_News_America_218.99-223.26_ago.mp4",
|
24 |
+
"Here are some frames of a video. Describe this video in detail.",
|
25 |
+
],
|
26 |
+
[" ./videos/2016-01-01_0100_US_KNBC_Channel_4_News_1877.19-1881.53_now.mp4",
|
27 |
+
"Here are some frames of a video. Describe this video in detail.",],
|
28 |
+
]
|
29 |
+
|
30 |
+
# Title, description, and article for the interface
|
31 |
+
title = "GSoC Super Raid Annotator"
|
32 |
+
description = "Gradio Demo for the MiniCPM-V 2.6 Vision Language Understanding and Generation model. This model can answer questions about videos in natural language. To use it, simply upload your video, type a question, and click 'submit', or click one of the examples to load them. Read more at the links below."
|
33 |
+
article = "<p style='text-align: center'><a href='https://github.com/OpenBMB/MiniCPM-V' target='_blank'>Model GitHub Repo</a> | <a href='https://huggingface.co/openbmb/MiniCPM-V-2_6' target='_blank'>Model Page</a></p>"
|
34 |
+
|
35 |
+
|
36 |
+
# Launch the interface
|
37 |
+
interface = gr.Interface(
|
38 |
+
fn=describe_video,
|
39 |
+
inputs=[video, query],
|
40 |
+
outputs=response,
|
41 |
+
examples=examples,
|
42 |
+
title=title,
|
43 |
+
description=description,
|
44 |
+
article=article,
|
45 |
+
theme="Soft",
|
46 |
+
allow_flagging="never",
|
47 |
+
)
|
48 |
+
interface.launch(debug=False)
|
requirements.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
numpy==1.26.4
|
2 |
+
Pillow==10.1.0
|
3 |
+
torch==2.1.2
|
4 |
+
torchvision==0.16.2
|
5 |
+
transformers==4.40.2
|
6 |
+
sentencepiece==0.1.99
|
7 |
+
https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.2/flash_attn-2.6.2+cu123torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
|
8 |
+
gradio
|
9 |
+
decord
|
src/__init__.py
ADDED
File without changes
|
src/utils.py
ADDED
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Importing the requirements
|
2 |
+
from PIL import Image
|
3 |
+
from decord import VideoReader, cpu
|
4 |
+
|
5 |
+
|
6 |
+
# Maximum number of frames to use
|
7 |
+
MAX_NUM_FRAMES = 64 # If CUDA OOM, set a smaller number
|
8 |
+
|
9 |
+
|
10 |
+
def encode_video(video_path):
|
11 |
+
"""
|
12 |
+
Encodes a video file into a list of frames.
|
13 |
+
|
14 |
+
Args:
|
15 |
+
video_path (str): The path to the video file.
|
16 |
+
|
17 |
+
Returns:
|
18 |
+
list: A list of frames, where each frame is represented as an Image object.
|
19 |
+
"""
|
20 |
+
|
21 |
+
def uniform_sample(l, n):
|
22 |
+
"""
|
23 |
+
Uniformly samples elements from a list.
|
24 |
+
|
25 |
+
Args:
|
26 |
+
- l (list): The input list.
|
27 |
+
- n (int): The number of elements to sample.
|
28 |
+
|
29 |
+
Returns:
|
30 |
+
list: A list of sampled elements.
|
31 |
+
"""
|
32 |
+
gap = len(l) / n
|
33 |
+
idxs = [int(i * gap + gap / 2) for i in range(n)]
|
34 |
+
return [l[i] for i in idxs]
|
35 |
+
|
36 |
+
# Read the video file and sample frames
|
37 |
+
vr = VideoReader(video_path, ctx=cpu(0))
|
38 |
+
sample_fps = round(vr.get_avg_fps() / 1) # FPS
|
39 |
+
frame_idx = [i for i in range(0, len(vr), sample_fps)]
|
40 |
+
|
41 |
+
# Uniformly sample frames if the number of frames is too large
|
42 |
+
if len(frame_idx) > MAX_NUM_FRAMES:
|
43 |
+
frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
|
44 |
+
|
45 |
+
# Extract frames from the video
|
46 |
+
frames = vr.get_batch(frame_idx).asnumpy()
|
47 |
+
frames = [Image.fromarray(v.astype("uint8")) for v in frames]
|
48 |
+
|
49 |
+
# Return video frames
|
50 |
+
return frames
|
src/video_model.py
ADDED
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Importing the requirements
|
2 |
+
import torch
|
3 |
+
from transformers import AutoModel, AutoTokenizer
|
4 |
+
import spaces
|
5 |
+
from src.utils import encode_video
|
6 |
+
|
7 |
+
|
8 |
+
# Device for the model
|
9 |
+
device = "cuda"
|
10 |
+
|
11 |
+
# Load the model and tokenizer
|
12 |
+
model = AutoModel.from_pretrained(
|
13 |
+
"openbmb/MiniCPM-V-2_6",
|
14 |
+
trust_remote_code=True,
|
15 |
+
attn_implementation="sdpa",
|
16 |
+
torch_dtype=torch.bfloat16,
|
17 |
+
)
|
18 |
+
model = model.to(device=device)
|
19 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
20 |
+
"openbmb/MiniCPM-V-2_6", trust_remote_code=True
|
21 |
+
)
|
22 |
+
model.eval()
|
23 |
+
|
24 |
+
|
25 |
+
@spaces.GPU(duration=100)
|
26 |
+
def describe_video(video, question):
|
27 |
+
"""
|
28 |
+
Describes a video by generating an answer to a given question.
|
29 |
+
|
30 |
+
Args:
|
31 |
+
- video (str): The path to the video file.
|
32 |
+
- question (str): The question to be answered about the video.
|
33 |
+
|
34 |
+
Returns:
|
35 |
+
str: The generated answer to the question.
|
36 |
+
"""
|
37 |
+
# Encode the video frames
|
38 |
+
frames = encode_video(video)
|
39 |
+
|
40 |
+
# Message format for the model
|
41 |
+
msgs = [{"role": "user", "content": frames + [question]}]
|
42 |
+
|
43 |
+
# Set decode params for video
|
44 |
+
params = {
|
45 |
+
"use_image_id": False,
|
46 |
+
"max_slice_nums": 1, # Use 1 if CUDA OOM and video resolution > 448*448
|
47 |
+
}
|
48 |
+
|
49 |
+
# Generate the answer
|
50 |
+
answer = model.chat(
|
51 |
+
image=None,
|
52 |
+
msgs=msgs,
|
53 |
+
tokenizer=tokenizer,
|
54 |
+
sampling=True,
|
55 |
+
stream=True,
|
56 |
+
top_p=0.8,
|
57 |
+
top_k=100,
|
58 |
+
temperature=0.7,
|
59 |
+
repetition_penalty=1.05,
|
60 |
+
max_new_tokens=2048,
|
61 |
+
system_prompt="You are an AI assistant specialized in visual content analysis. Given a video and a related question, analyze the video thoroughly and provide a precise and informative answer based on the visible content. Ensure your response is clear, accurate, and directly addresses the question.",
|
62 |
+
**params
|
63 |
+
)
|
64 |
+
|
65 |
+
# Return the answer
|
66 |
+
return "".join(answer)
|