Cursusaanbod

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

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
  • Legal, medical, or technical language handling

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

Vereisten

  • An 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
  • Organizations exploring speech recognition for automation
 14 Uren

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