To analyze and generate captions for videos using LangChain, you can leverage the langchain_experimental.video_captioning module. Below is a comprehensive guide, including the necessary Python classes for handling video and audio models and generating captions.
- BaseModel: This class serves as a base for video and audio models.
- VideoModel: Represents a video segment, including start and end times and a description of the visual content.
- AudioModel: Represents an audio segment, including start and end times and subtitle text.
- CaptionModel: Represents a caption generated from video or audio models.
This class serves as the base for other models.
from datetime import datetime
from typing import Any
class BaseModel:
def __init__(self, start_time: int, end_time: int) -> None:
self._start_time = start_time
self._end_time = end_time
@property
def start_time(self) -> int:
return self._start_time
@start_time.setter
def start_time(self, value: int) -> None:
self._start_time = value
@property
def end_time(self) -> int:
return self._end_time
@end_time.setter
def end_time(self, value: int) -> None:
self._end_time = value
def __str__(self) -> str:
return f"start_time: {self.start_time}, end_time: {self.end_time}"
@classmethod
def from_srt(cls, start_time: str, end_time: str, *args: Any) -> "BaseModel":
return cls(
cls._srt_time_to_ms(start_time),
cls._srt_time_to_ms(end_time),
*args
)
@staticmethod
def _srt_time_to_ms(srt_time_string: str) -> int:
time_format = "%H:%M:%S,%f"
dt = datetime.strptime(srt_time_string, time_format)
ms = dt.microsecond // 1000
return dt.second * 1000 + msRepresents a video segment.
class VideoModel(BaseModel):
def __init__(self, start_time: int, end_time: int, image_description: str) -> None:
super().__init__(start_time, end_time)
self._image_description = image_description
@property
def image_description(self) -> str:
return self._image_description
@image_description.setter
def image_description(self, value: str) -> None:
self._image_description = value
def __str__(self) -> str:
return f"{super().__str__()}, image_description: {self.image_description}"
def similarity_score(self, other: "VideoModel") -> float:
self_tokenized = set(
word.lower().rstrip("s") for word in self.image_description.split()
)
other_tokenized = set(
word.lower().rstrip("s") for word in other.image_description.split()
)
common_words = self_tokenized.intersection(other_tokenized)
similarity_score = (
len(common_words) / max(len(self_tokenized), len(other_tokenized)) * 100
)
return similarity_scoreRepresents an audio segment.
class AudioModel(BaseModel):
def __init__(self, start_time: int, end_time: int, subtitle_text: str) -> None:
super().__init__(start_time, end_time)
self._subtitle_text = subtitle_text
@property
def subtitle_text(self) -> str:
return self._subtitle_text
@subtitle_text.setter
def subtitle_text(self, value: str) -> None:
self._subtitle_text = value
def __str__(self) -> str:
return f"{super().__str__()}, subtitle_text: {self.subtitle_text}"Represents a caption generated from video or audio models.
class CaptionModel(BaseModel):
def __init__(self, start_time: int, end_time: int, closed_caption: str) -> None:
super().__init__(start_time, end_time)
self._closed_caption = closed_caption
@property
def closed_caption(self) -> str:
return self._closed_caption
@closed_caption.setter
def closed_caption(self, value: str) -> None:
self._closed_caption = value
def add_subtitle_text(self, subtitle_text: str) -> "CaptionModel":
self._closed_caption = self._closed_caption + " " + subtitle_text
return self
def __str__(self) -> str:
return f"{super().__str__()}, closed_caption: {self.closed_caption}"
def to_srt_entry(self, index: int) -> str:
def _ms_to_srt_time(ms: int) -> str:
hours = int(ms // 3600000)
minutes = int((ms % 3600000) // 60000)
seconds = int((ms % 60000) // 1000)
milliseconds = int(ms % 1000)
return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
return "\n".join(
[
f"""{index}
{_ms_to_srt_time(self._start_time)} --> {_ms_to_srt_time(self._end_time)}
{self._closed_caption}""",
]
)
@classmethod
def from_audio_model(cls, audio_model: AudioModel) -> "CaptionModel":
return cls(
audio_model.start_time,
audio_model.end_time,
audio_model.subtitle_text
)
@classmethod
def from_video_model(cls, video_model: VideoModel) -> "CaptionModel":
return cls(
video_model.start_time,
video_model.end_time,
f"[{video_model.image_description}]",
)You can now use these models to generate captions from video and audio segments.
# Example usage
video_model = VideoModel(start_time=0, end_time=10000, image_description="A person is walking")
audio_model = AudioModel(start_time=5000, end_time=15000, subtitle_text="Hello, how are you?")
caption_from_video = CaptionModel.from_video_model(video_model)
caption_from_audio = CaptionModel.from_audio_model(audio_model)
print(caption_from_video)
print(caption_from_audio)Use the CombineProcessor class to handle overlapping video and audio segments and generate captions.
from langchain_experimental.video_captioning.services.combine_service import CombineProcessor
# Initialize your language model (OpenAI in this case)
llm = OpenAI(api_key="YOUR_OPENAI_API_KEY")
# Create the CombineProcessor
processor = CombineProcessor(llm=llm, verbose=True, char_limit=100)
# List of video and audio models
video_models = [VideoModel(start_time=0, end_time=10, image_description="A person is walking")]
audio_models = [AudioModel(start_time=5, end_time=15, subtitle_text="Hello, how are you?")]
# Process to get captions
captions = processor.process(video_models, audio_models)
for caption in captions:
print(caption.closed_caption)This approach allows you to analyze and generate captions for videos using LangChain's experimental video captioning capabilities. For more detailed information and advanced usage, refer to the LangChain API documentation: