wehub-resource-sync
33 lines
1.9 KiB
Markdown
33 lines
1.9 KiB
Markdown
# Lecture Tool
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Welcome to Lecture Tool! This is an AI application that leverages `llmware` to transcribe and analyze college lecture videos.
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---
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### Features
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1. Create `Libraries` to group transcripts and store them persistently.
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2. Transcribe audio files using Whisper (built into the `llmware` library) and store them in a `Library`.
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3. Ask general questions and questions about lecture content.
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4. Summarize lecture content.
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5. View all generated transcripts.
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Each of these five features is implemented in its own file in the `pages` folder.
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---
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### Prerequisites
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1. *Python libraries*: the only required libraries to be installed are `streamlit` and `llmware`. You can install them from the `requirements.txt` file.
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2. *MongoDB*: it is used to store lecture transcripts. The easiest way to install it is to use the Docker Compose file in the [LLMWare repository](https://github.com/llmware-ai/llmware/blob/main/docker-compose_mongo_milvus.yaml).
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3. *FFmpeg*: it is used to convert MP3 files to WAV files that are compatible with Whipser. If you intend to use MP3 files instead of WAV files, you can [download FFmpeg here](https://www.ffmpeg.org/download.html). You will likely need to restart your computer after installation.
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---
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### Usage
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To run the program, ensure that you have `streamlit` installed. In your terminal, navigate to the `lecture_tool` directory and run `streamlit run Home.py`.
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By default, Streamlit supports file uploads up to 200 MB. To increase this limit, run `streamlit run Home.py --server.maxUploadSize fileSize`, ensuring to replace `fileSize` with the maximum file size you want to upload in megabytes. For example, use 500 if you plan to upload audio files up to 500 MB in size.
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Sample MP3 and WAV audio files to use the application with are available in the `sample_audio_files` directory.
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The `saved_files` directory is used as a temporary location in the application's implementation and should not be modified by a user.
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