Audio Extraction & Speech Transcription
This project focuses on audio extraction from video sources and speech-to-text transcription. It demonstrates techniques for extracting audio from YouTube videos and local video files, converting between audio formats (MP3, WAV, M4A), and transcribing speech to text using Google Speech Recognition API. The project handles multiple audio tracks, processes long audio files in chunks (60-second segments), and supports various audio formats. It includes error handling for transcription failures, format conversion capabilities, and multi-source support for both YouTube videos and local files.
Overview
This project focuses on audio extraction from video sources and speech-to-text transcription. It demonstrates techniques for extracting audio from YouTube videos and local video files, converting between audio formats (MP3, WAV, M4A), and transcribing speech to text using Google Speech Recognition API. The project handles multiple audio tracks, processes long audio files in chunks (60-second segments), and supports various audio formats. It includes error handling for transcription failures, format conversion capabilities, and multi-source support for both YouTube videos and local files.
Key Features
Audio extraction from YouTube videos and local video files
Format conversion between MP3, WAV, and M4A
Speech-to-text transcription using Google Speech Recognition API
Chunk processing for long audio files (60-second segments)
Multi-track audio processing (Piste30, Piste_87_2, Piste_90_2)
Error handling for transcription failures and API errors
Language support (English, French)
Video file handling with MoviePy
Batch processing capabilities
Temporary file management for chunk processing
pages.portfolio.projects.audio_extraction_speech_transcription.features.10
Technical Highlights
Extracted audio from YouTube videos and local video files
Implemented format conversion between multiple audio formats
Created chunk-based processing for long audio files
Integrated Google Speech Recognition API for transcription
Handled multiple audio tracks with organized file management
Implemented error handling for robust transcription workflows
Challenges and Solutions
Long Audio Files
Split audio into 60-second chunks to handle Google Speech Recognition API limits
Format Compatibility
Used PyDub to convert MP3/M4A to WAV format required for speech recognition
YouTube Download Issues
Implemented alternative methods with MoviePy and direct file processing
FFmpeg Dependencies
Configured proper installation and path settings for FFmpeg/FFprobe
API Rate Limits
Implemented chunk processing and error handling for API limitations
Memory Management
Used chunk-based processing and temporary file cleanup for efficient memory usage
Technologies
Audio Processing
Speech Recognition
Download Tools
Audio Formats
Data
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Audio Processing Pipeline with Extraction, Conversion, and Transcription
- Category
- Data Science