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Wearable Sensor Research

Audio Analyzer and Splicer
January 2024 - Present

Description

This project focuses on processing and analyzing audio and video data collected from wearable sensors. It involves generating transcripts from media files and extracting video or audio clips for each word identified in the transcripts. The provided tools and scripts support research in wearable technology and human-computer interaction.

Getting Started

Dependencies

  • Operating System: Windows 10, macOS, or Linux
  • Conda: Required for managing the Python environment
  • Python Version: Python 3.9 (managed via Conda)
  • FFmpeg: Must be installed and accessible via Conda

Installation

1. Install Conda

Download and install Anaconda or Miniconda:

2. Create a Conda Environment

Open your terminal or command prompt and create a new Conda environment:

conda create -n whisper_env python=3.9

Activate the environment:

conda activate whisper_env

3. Install PyTorch

Install PyTorch in the Conda environment:

conda install pytorch torchvision torchaudio cpuonly -c pytorch

4. Install Whisper Timestamped

Install the whisper-timestamped package from GitHub:

pip install git+https://github.com/linto-ai/whisper-timestamped

5. Install FFmpeg

Install FFmpeg via Conda:

conda install -c conda-forge ffmpeg

6. Install Additional Dependencies

Run the following commands to install additional packages:

pip install numpy
pip install ffmpeg-python
pip install moviepy

Usage

After setting up the environment, activate the whisper_env environment before running your scripts:

conda activate whisper_env

You can then use the scripts to generate transcripts and extract word-level clips from your audio or video files.

Conclusion

This project enables advanced analysis of wearable sensor data through speech transcription and media processing. Make sure to keep your Conda environment activated when working on related scripts to ensure all dependencies are properly loaded.