Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

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

Fundamentals of Audio Preprocessing and Transcription

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

Practical Experience with Whisper and External APIs

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

Addressing Language Variations, Accents, and Domain-Specific Needs

• Managing multiple languages and diverse accents
• Implementing custom vocabularies and improving noise resilience
• Processing specialized language in legal, medical, or technical contexts

Structuring Output and System Integration

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

Application of Use Cases in Labs

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

Assessment, Constraints, and Ethical Considerations

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

Conclusion and Future Directions