This project is designed to parse text from various media types: audio (.wav), video (.mp4), and text documents (.pdf). The implementation utilizes Python and its libraries, relying exclusively on free APIs and libraries for unlimited usage.
- Parse Text from Video
- Implementation: Extracts audio from the video in .wav format using the Moviepy library. Processes the audio through text extraction, reusing the logic implemented for audio parsing.
- Issues Faced: Alternative libraries, such as SpeechRecognition, proved less efficient than Moviepy.
- Parse Text from Audio
- Implementation: Utilized the SpeechRecognition library with the Google Web Speech-to-Text service for audio transcription. Dealt with limitations of 120-second processing by splitting audio into 115-second chunks using pydub.silence. Errors such as UnknownValueError and RequestError were managed with appropriate fallbacks.
- Challenges: Processing long audio files is time-intensive (e.g., 15-minute audio may take 30-40 minutes). Paid APIs with better performance were avoided due to limited free usage.
- Parse Text from PDF
- Implementation: Used the PyMuPDF library to extract clean and structured text.
- Challenges: Preservation of tabular data was not supported natively. However, the row structure of tabular data was maintained by outputting each column in a new line. Alternatives like PyPDF2, Textract, and Tika were tested but found less efficient.
- Text Extraction:
SpeechRecognition,PyMuPDF - Audio Processing:
Moviepy,pydub - Error Handling: Custom error management for transcription inconsistencies
Limited accuracy and speed of the Google Web Speech-to-Text service. Inefficiency in preserving tabular data in PDF files. For further improvements, exploring better transcription APIs and tabular-specific libraries like Tabulapy is recommended.