Speech Recognition and Transcription Using AI Training Course

Overview of Speech Recognition Technologies

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

Audio Preprocessing and Transcription Basics

• Managing audio formats and sample rates
• Techniques for cleaning, trimming, and segmenting audio
• Generating text from audio: real-time versus batch processing

Practical Work with Whisper and Other APIs

• Installing and utilising OpenAI Whisper
• Leveraging cloud APIs (such as Google and Azure) for transcription
• Comparing performance, latency, and cost-effectiveness

Language, Accents, and Domain Adaptation

• Handling multiple languages and diverse accents
• Implementing custom vocabularies and enhancing noise tolerance
• Processing legal, medical, or highly technical language

Output Formatting and Integration

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

Use Case Implementation Labs

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

Evaluation, Limitations, and Ethics

• Accuracy metrics and model benchmarking strategies
• Addressing bias and fairness in speech models
• Privacy and compliance considerations

Summary and Next Steps