Audio2Text - Speech Recognition System
Project Overview
Audio2Text is an advanced speech recognition system that converts spoken language into written text with high accuracy. The project leverages state-of-the-art deep learning models to transcribe audio recordings, supporting multiple audio formats and handling various acoustic conditions.
The system is designed for applications such as automated transcription services, voice-controlled interfaces, meeting minutes generation, and accessibility tools for hearing-impaired users. It implements noise reduction and speaker diarization to improve transcription quality in challenging audio environments.
Key Features
- High-accuracy speech-to-text conversion
- Support for multiple audio formats (WAV, MP3, FLAC)
- Real-time and batch processing modes
- Noise reduction and audio preprocessing
- Speaker diarization (identifying different speakers)
- Timestamp generation for transcriptions
- Multi-language support capability
Technology Stack
Python
Primary programming language for audio processing and model implementation
Deep Learning Models
Advanced neural networks for speech recognition, including transformers and RNNs
Audio Processing Libraries
Tools like Librosa and PyDub for audio manipulation and feature extraction
Results & Impact
Audio2Text achieves excellent transcription accuracy across various audio qualities and acoustic conditions. The system significantly reduces the time required for manual transcription, making it valuable for journalists, researchers, and content creators. Its robust performance in noisy environments makes it suitable for real-world applications.
Future Enhancements
- Enhanced support for accents and dialects
- Real-time streaming transcription
- Integration with popular video conferencing platforms
- Custom vocabulary and domain adaptation
- Automated punctuation and formatting
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