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Course Outline
Introduction to Speech Recognition Technologies
- Historical context and the evolution of speech recognition
- Acoustic models, language models, and decoding processes
- Cutting-edge architectures: RNNs, transformers, and Whisper
Audio Preprocessing and Fundamentals of Transcription
- Managing various audio formats and sample rates
- Techniques for cleaning, trimming, and segmenting audio files
- Converting audio to text: real-time versus batch processing
Practical Application of Whisper and External APIs
- Setting up and utilizing OpenAI Whisper
- Utilizing cloud-based APIs (such as Google and Azure) for transcription
- Analyzing performance, latency, and cost efficiency
Language Variations, Accents, and Domain-Specific Adaptation
- Processing multiple languages and diverse accents
- Implementing custom vocabularies and improving noise resistance
- Handling 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
- Integrating transcribed data into applications or database systems
Practical Use Case Labs
- Transcribing content from meetings, interviews, or podcasts
- Developing voice-to-text command systems
- Generating real-time captions for video or audio streams
Assessment, Constraints, and Ethical Considerations
- Metrics for accuracy and benchmarking models
- Addressing bias and fairness in speech models
- Navigating privacy concerns and compliance requirements
Recap and Future Directions
Requirements
- A foundational grasp of general AI and machine learning principles
- Proficiency with audio or media file formats and associated tools
Target Audience
- Data scientists and AI engineers specializing in voice data
- Software developers creating applications based on transcription
- Organizations seeking to automate processes using speech recognition
14 Hours
