code slash

Inspiration

The inspiration behind this project was to address the challenge of quickly and efficiently transcribing videos stored locally. Often, professionals, researchers, and content creators have a large number of video files on their local machines that need to be transcribed for documentation, subtitles, or further analysis.

What it does

This solution automatically scans a local directory for video files, extracts the audio from each video, and then transcribes the audio into text. The transcription is performed using Google's Speech Recognition API. Once transcribed, the text is either displayed on the console or saved as a text file alongside the original video file. This makes it easy for users to process multiple video files at once, without having to manually extract and transcribe each one.

How we built it

The tool was built using Python, utilizing several key libraries:

os: For navigating the local directory structure and handling file paths. pydub: To extract audio from video files and convert it into a format suitable for transcription. speech_recognition: To transcribe the extracted audio into text using Google’s Speech Recognition API. IPython.display.Audio: To play back the audio within a Jupyter Notebook for verification

Challenges we ran into

One of the primary challenges was handling different video formats and ensuring the audio extraction process was robust across various file types. Another challenge was managing the performance when processing multiple large video files, as well as ensuring the accuracy of the transcriptions in cases of poor audio quality or background noise. Additionally, we faced limitations with the Google Speech Recognition API, such as handling long-duration audio files and managing API request limits.

Accomplishments that we're proud of

We are proud of creating a fully automated solution that can handle an entire directory of video files, extracting and transcribing them without user intervention. The tool's ability to accurately transcribe various video formats and manage the output efficiently is a significant accomplishment. We also successfully integrated multiple Python libraries to create a seamless workflow, demonstrating strong problem-solving and technical skills.

What we learned

Throughout this project, we learned a lot about audio processing, including handling different video formats and converting them to suitable audio formats for transcription. We gained insights into the intricacies of speech recognition and the factors that can influence transcription accuracy.

What's next for code slash

Moving forward, we plan to enhance the tool by adding more features such as batch processing, support for additional languages, and improved handling of long-duration files. We are also considering adding a graphical user interface (GUI) to make the tool more user-friendly, allowing users to select directories, view progress, and manage transcriptions more easily.

Built With

• ipython.display.audio
• pydub
• python
• speech-recognition