Speech Recognition and Transcription Using AI Training Course

Introduction to Speech Recognition Technologies

• The historical development and evolution of speech recognition
• Roles of 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: comparing real-time and batch processing

Practical Work with Whisper and Alternative APIs

• Setup and utilization of OpenAI Whisper
• Accessing cloud-based APIs (such as Google and Azure) for transcription
• Analyzing differences in performance, latency, and cost

Managing Language Variations, Accents, and Domain-Specific Adaptation

• Processing content across multiple languages and accents
• Implementing custom vocabularies and improving noise resistance
• Addressing specialized terminology in legal, medical, or technical contexts

Structuring Output and System Integration

• Incorporating timestamps, punctuation, and speaker identification
• Exporting data to text, SRT, or JSON formats
• Embedding transcriptions into applications or database systems

Practical Labs for Real-World Use Cases

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

Model Evaluation, Constraints, and Ethical Considerations

• Assessing accuracy metrics and conducting model benchmarking
• Examining bias and fairness within speech models
• Navigating privacy issues and compliance standards

Conclusion and Future Directions