Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

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

Fundamentals of Audio Preprocessing and Transcription

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

Practical Application with Whisper and Other APIs

• Installation and utilization of OpenAI Whisper
• Integrating cloud APIs (Google, Azure) for transcription tasks
• Comparing performance metrics, latency, and associated costs

Language Variations, Accents, and Domain Adaptation

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

Structuring Output and System Integration

• Enriching output with timestamps, punctuation, and speaker identification
• Exporting data into text, SRT, or JSON formats
• Embedding transcription results into applications or databases

Use Case Implementation Labs

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

Assessment, Constraints, and Ethical Considerations

• Defining accuracy metrics and benchmarking models
• Addressing bias and fairness within speech models
• Navigating privacy standards and compliance requirements

Recap and Future Directions