Speech Recognition and Transcription Using AI Training Course

Foundations of Speech Recognition Technologies

• The history and progression of speech recognition
• Acoustic models, language models, and decoding processes
• Contemporary architectures: RNNs, transformers, and Whisper

Audio Preprocessing and Fundamental Transcription

• Managing audio formats and sample rates
• Audio cleaning, trimming, and segmentation techniques
• Converting audio to text: real-time versus batch processing

Practical Application with Whisper and External APIs

• Setting up and utilizing OpenAI Whisper
• Invoking cloud APIs (Google, Azure) for transcription services
• Analyzing performance, latency, and cost implications

Language Variations, Accents, and Domain Adaptation

• Navigating multiple languages and regional accents
• Implementing custom vocabularies and managing noise tolerance
• Processing legal, medical, or technical terminology

Formatting Output and System Integration

• Incorporating timestamps, punctuation, and speaker identification
• Exporting results to text, SRT, or JSON formats
• Integrating transcriptions into applications or databases

Implementation Labs for Real-World Scenarios

• Transcribing meetings, interviews, or podcast content
• Developing voice-to-text command systems
• Generating real-time captions for video/audio streams

Assessment, Limitations, and Ethical Considerations

• Accuracy metrics and model benchmarking strategies
• Bias and fairness within speech recognition models
• Privacy and regulatory compliance factors

Conclusion and Future Directions