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Duration 14 hours (2 days)
Course Outline
Overview of Speech Recognition Technologies
- Historical evolution of speech recognition
- Acoustic models, language models, and decoding processes
- Modern architectures: RNNs, transformers, and Whisper
Audio Preprocessing and Transcription Fundamentals
- Managing audio formats and sample rates
- Cleaning, trimming, and segmenting audio files
- Converting audio to text: real-time versus batch processing
Practical Work with Whisper and Other APIs
- Installation and utilisation of OpenAI Whisper
- Integrating cloud APIs (Google, Azure) for transcription tasks
- Benchmarking performance, latency, and cost-effectiveness
Language, Accents, and Domain Adaptation
- Processing multiple languages and diverse accents
- Implementing custom vocabularies and noise tolerance settings
- Handling legal, medical, or technical terminology
Output Formatting and System Integration
- Incorporating timestamps, punctuation, and speaker labels
- Exporting data into text, SRT, or JSON formats
- Integrating transcriptions into applications or databases
Use Case Implementation Labs
- Transcribing meetings, interviews, or podcasts
- Developing voice-to-text command systems
- Generating real-time captions for video/audio streams
Evaluation, Limitations, and Ethical Considerations
- Accuracy metrics and model benchmarking techniques
- Addressing bias and fairness in speech models
- Privacy and compliance considerations
Summary and Next Steps
Requirements
- A solid 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
- Organisations exploring speech recognition for automation purposes
