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Course Outline
Overview of Speech Recognition Technologies
- The history and evolution of speech recognition.
- Understanding acoustic models, language models, and decoding mechanisms.
- Modern architectures: RNNs, transformers, and Whisper.
Audio Preprocessing and Transcription Basics
- Managing audio formats and sample rates.
- Cleaning, trimming, and segmenting audio files.
- Generating text from audio: real-time versus batch processing.
Hands-on with Whisper and Other APIs
- Installing and utilizing OpenAI Whisper.
- Utilizing cloud APIs (such as Google and Azure) for transcription.
- Comparing performance, latency, and cost implications.
Language, Accents, and Domain Adaptation
- Working effectively with multiple languages and accents.
- Implementing custom vocabularies and ensuring noise tolerance.
- Handling specialized language in legal, medical, or technical contexts.
Output Formatting and Integration
- Incorporating timestamps, punctuation, and speaker labels.
- Exporting data to 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.
- Providing real-time captions for video or audio streams.
Evaluation, Limitations, and Ethics
- Assessing accuracy metrics and performing model benchmarking.
- Addressing bias and fairness issues in speech models.
- Considering privacy and regulatory compliance.
Summary and Next Steps
Requirements
- A foundational understanding of general AI and machine learning concepts.
- Familiarity with audio or media file formats and associated tools.
Audience
- Data scientists and AI engineers dealing with voice data.
- Software developers creating applications centered on transcription.
- Organizations considering speech recognition for automation purposes.
14 Hours
