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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
- Handling 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.
- Calling 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 concepts.
- Familiarity with audio or media file formats and tools.
Audience
- Data scientists and AI engineers working with voice data.
- Software developers building transcription-based applications.
- Organisations exploring speech recognition for automation.
14 Hours
