Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• Historical context and the evolution of speech recognition
• Acoustic models, language models, and decoding mechanisms
• Contemporary architectures: RNNs, transformers, and Whisper

Audio Preprocessing and Fundamental Transcription

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

Practical Application with Whisper and External APIs

• Installing and utilizing OpenAI Whisper
• Integrating cloud-based APIs (Google, Azure) for transcription
• Benchmarking performance, latency, and cost efficiency

Linguistic Diversity, Accents, and Domain-Specific Adaptation

• Processing multiple languages and diverse accents
• Implementing custom vocabularies and enhancing noise tolerance
• Handling specialized terminology in legal, medical, or technical contexts

Output Structuring and System Integration

• Incorporating timestamps, punctuation, and speaker identification labels
• Exporting data into text, SRT, or JSON formats
• Embedding transcriptions into applications or databases

Practical Use Case Labs

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

Assessment, Constraints, and Ethical Considerations

• Defining accuracy metrics and benchmarking models
• Addressing bias and fairness in speech recognition models
• Navigating privacy and compliance requirements

Course Summary and Recommended Next Steps