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Course Outline
Overview of Speech Recognition Technologies
- History and evolution of speech recognition
- Acoustic models, language models, and decoding
- Modern architectures: RNNs, transformers, and Whisper
Audio Preprocessing and Transcription Basics
- Managing audio formats and sample rates
- Cleaning, trimming, and segmenting audio
- Generating text from audio: real-time vs batch processing
Hands-on with Whisper and Other APIs
- Installing and using OpenAI Whisper
- Utilizing cloud APIs (Google, Azure) for transcription
- Comparing performance, latency, and cost
Language, Accents, and Domain Adaptation
- Working with multiple languages and accents
- Custom vocabularies and noise tolerance
- Handling legal, medical, or technical language
Output Formatting and Integration
- Adding timestamps, punctuation, and speaker labels
- Exporting to text, SRT, or JSON formats
- Integrating transcriptions into apps or databases
Use Case Implementation Labs
- Transcribing meetings, interviews, or podcasts
- Voice-to-text command systems
- Real-time captions for video/audio streams
Evaluation, Limitations, and Ethics
- Accuracy metrics and model benchmarking
- Bias and fairness in speech models
- Privacy and compliance considerations
Summary and Next Steps
Requirements
- A foundational understanding of general AI and machine learning principles
- Familiarity with audio or media file formats and associated tools
Target Audience
- Data scientists and AI engineers working with voice data
- Software developers creating transcription-based applications
- Organizations exploring speech recognition for automation purposes
14 Hours
